Clinical risk factors for in-hospital mortality in older adults with HIV infection: findings from a South African hospital administrative dataset

Introduction The proportion of older South African adults (aged ≥50 years old) with HIV infection requiring hospitalization is likely to increase in the near future. Clinical risk factors for in-hospital mortality (IHM) in these patients are not well described. We aimed to identify clinical risk factors associated with IHM and their overall contribution towards IHM in older South African adults with HIV infection. Methods Clinical data for 690 older adults with HIV infection was obtained from the hospital administrative database at the Hlabisa Hospital in KwaZulu-Natal, South Africa. Logistic regression was used to determine independent clinical risk factors for IHM. Population-attributable fractions (PAFs) were calculated for all independent clinical risk factors identified. Results Male gender (p=0.005), CD4 count <350 cells/mm3 (p=0.035), unknown CD4 count (p=0.048), tuberculosis (p=0.033) and renal failure (p=0.013) were independently associated with IHM. Male gender contributed the most to IHM (PAF=0.22), followed by unknown CD4 count (PAF=0.14), tuberculosis (PAF=0.12), renal failure (PAF=0.06) and CD4 count <350 cells/mm3 (PAF=0.01). Conclusion Although further research is required to confirm our findings, there is potential for these clinical risk factors identified in our study to be used to stratify patient risk and reduce IHM in older adults with HIV infection.


Introduction
Dramatic scale-up in the provision of antiretroviral therapy (ART) for HIV-infection in sub-Saharan Africa continues to improve the quality of life and radically reduce early mortality associated with HIV [1].
In sub-Saharan Africa it is estimated that ART coverage reached HIV-infected provided ART is initiated early (CD4 count >250 cells/µL) [3]. Therefore, as scale-up of ART in sub-Saharan Africa continues to rapidly expand, the life expectancy of the HIV-infected population continue to improve, resulting in a temporal transition of the HIV epidemic toward the older age groups. In Africa, the World Health Organization (WHO) defines older age as 50 years or more, due to new roles and loss of previous roles in society [4]. An increase in the prevalence of HIV in persons older than 50 years of age has already been reported in several developed countries. In the United States ART was first introduced in 1996, after which the prevalence of HIV in older adults (≥ 50 years old) grew from 17% in 2001 to 30% in 2008 [5,6].
In Italy the proportion of older adults living with HIV increased from 4.9% in the years 1982-1990 to 15.9% in the years 2000-2005 [7].
Older adults in 2012 represented 9.5% (608 000 persons) of the total HIV burden in South Africa [8]. A study in rural South Africa quantified the aging of the HIV epidemic and predicted that the number of HIV-infected older adults would increase by 100% between 2004 and 2025 [9]. Moreover there is an increasing trend in the incidence of HIV in older adults reported in rural settings where health services are now becoming accessible [8]. Inevitably a proportion of these older adults with HIV infection will require hospitalization for various HIV-related or HIV-unrelated conditions during their lifetime [10][11][12]. Studies on HIV-infected individuals from the United States and Greece have shown higher comorbidities, longer lengths of admission and more frequent hospitalizations in older patients compared to younger patients [5,13,14]. Furthermore studies suggest higher inpatient mortality in HIV-infected older adult patients when compared to their HIVinfected younger counterparts [14]. In-hospital mortality (IHM) is associated with higher health care expenditure and is also an important indicator of a public health system´s response to the HIV epidemic [14,15]. Considering the progressively high levels of IHM in older adults infected with HIV and its potential impact on resource utilization within public health systems, it is important that methods such as risk stratification systems be developed, that aim to reduce IHM in these patients. Risk stratification systems involve the identification of independent clinical risk factors within a population at risk of a poor outcome [16]. However, clinical risk factors that contribute to IHM in older adults infected with HIV are not well described, particularly in the South African context where the burden of HIV in this sub-population is amongst the highest in the world. We therefore sought to identify clinical risk factors associated with IHM and their overall contribution toward this poor outcome in older adults with HIV infection admitted to a South African hospital.

Methods
Study design and setting: This study was a retrospective analysis of data collected at the Hlabisa Hospital between January 2011 and February 2015. The hospital provides services to over 230 000 people in the Hlabisa district, Mtubatuba and parts of the "Big Five" municipalities in the Northern KwaZulu-Natal Province, South Africa.
The overall HIV incidence in the Hlabisa district has been estimated at 3.2 per 100 person years while the district prevalence of HIV in the older adult is estimated at 9.5% and predicted to further rise [8]. Other characteristics of the Hlabisa population are described elsewhere [17]. Univariate statistical methods: Gender, ART status, CD4 count, and co-morbidities were converted to categorical variables.
Categorical data was analysed using χ 2 tests or Fishers Exact test, where appropriate. Age data was treated as a continuous variable.
When tested for normality using the Shapiro-Wilk test, age data was not found to be normally distributed and was therefore analysed using a Mann-Whitney U test. Results of the univariate statistical testing are presented as frequencies and percentages, or medians and interquartile ranges (IQRs), where appropriate.

Multivariate statistical methods: Logistic regression was used
to identify independent associations between clinical characteristics and IHM. Clinical characteristics with p<0.2 from the univariate testing were included in the logistic regression analysis. This purposeful selection of clinical characteristics ensured that we did not potentially violate the "10 outcome events per single variable" rule of thumb for logistic regression analyses and also ensured that we would obtain a parsimonious regression model [20,21]. A Hosmer-Lemeshow test was used to measure model fit. Results were presented as odds ratios (OR) with a confidence interval of 95% (CIs). Results with a p-value <0.05 were considered statistically significant.

Population-attributable fraction (PAF):
The PAF is defined as the proportion of cases in the population that would not have occurred in the absence of the exposure [22]. In this study the PAF was calculated for each independent risk factor identified from the binary logistic regression analysis (clinical characteristics with p<0.05 in the regression model) using conventional methods [22].

Results
Following application of the relevant inclusion and exclusion criteria (Table 1)  tuberculosis (p=0.033) and renal failure (p=0.013)) were found to be independently associated with a higher risk of IHM in the logistic regression analysis (Table 3). When entered into the logistic regression analysis, variables such as essential hypertension, pneumonia (excluding that caused by TB) and mycosis were not independently associated with an increased risk of IHM (Table 3).
The Hosmer-Lemeshow test indicated adequate regression model fit (p>0.05). The PAF obtained for the independent risk factors obtained from the logistic regression analysis (Table 4)  infections. Acute renal failure is estimated to occur in up to 30% of patients with HIV infection, and is much more common in this patient group when compared to HIV-uninfected patients [15,27].
The implications of renal failure on subsequent patient outcomes are marked, often linked with higher morbidity and mortality in afflicted patients [28][29][30]. This might explain our findings for a 91% higher risk of IHM in patients with renal failure, with 6 in every 100 inpatient deaths in our study being attributed to renal failure.
Our study did not identify independent associations between noncommunicable comorbid disease (besides renal failure) and IHM.
This finding may be related to infectious diseases contributing to mortality in a rural HIV-infected population rather than noncommunicable diseases. This is further supported by the finding of communicable conditions comprising the majority of the most common patient comorbidities in our study. However, it is important to note that South Africa has begun a state of epidemiological transition, whereby higher disease burden appears to be shifting away from communicable diseases toward non-communicable diseases [31,32]. It is likely that in the future non-communicable comorbid diseases will become more important in the context of patient outcomes in South Africa. Increasing age above 50 years old was also not associated with a higher risk of IHM in this study.
Increasing age is usually associated with increased noncommunicable disease comorbidity [33], which potentially increases the risk of future poor patient outcomes. However, we report a low burden of communicable disease in this study, despite the age of our study population. This might explain the observed lack of association between age and IHM in our study. No independent associations (either protective or harmful) with IHM could be established for anaemia and antiretroviral therapy status in our study. It is possible that anaemia was under-diagnosed, as anaemia is usually described as a common haematological abnormality in patients with HIV infection which is associated with poor survival [34]. In the general HIV-infected population, antiretroviral therapy use is associated with a reduction in mortality with HIV-infected Page number not for citation purposes 5 individuals on ART now achieving near-normal life expectancies [3,35]. Notably, almost half our study population was receiving antiretroviral therapy, which might explain our findings for this characteristic.
Study limitations: Data related to a number of potentially relevant patient clinical characteristics, for example body mass index, were not collected as part of the hospital administrative database.
Furthermore, data related to medication use (other than antiretroviral therapy) and laboratory results (other than CD4 count measurements) were not collected. As such we were unable to investigate the impact of these clinical characteristics on the incidence of IHM. The specific pathological cause of death was indeterminable from the hospital administrative database, and we therefore describe all-cause mortality in this study. Future studies employing a prospective study design with an emphasis on collecting data on potentially relevant population-specific characteristics are required to overcome the aforementioned study limitations. There were also several limitations identified which can only be overcome through health system strengthening. For instance, this study limited its analysis to inpatient outcomes as patient survival status following hospital discharge could not be reliably ascertained. Patient tracking and use of community caregivers to conduct home visits might improve follow-up of discharge patients [36]. In addition, a large proportion of the CD4 count measurement data was missing. The challenge of missing CD4 count measurements has been identified elsewhere [37], and it once again represents a challenge which can only be overcome through health systems approaches such as the rollout of point of care CD4 count machines and the development of relevant checklists to improve CD4 count measurement recording [38]. The data used in our study was collected from a single, rural hospital and the findings of this study may not be generalizable to other  Tables and figure   Table 1: Inclusion and exclusion criteria used to derive the study population     Patients with invalid or missing discharge ICD-10 codes